Accuracy of predictive equations for resting energy expenditure estimation in mechanically ventilated Thai patients

Abstract Background: Indirect calorimetry (IC) is the most precise approach for estimating calorie demand in critically ill patients. Despite this, owing to unaffordable devices, it is rarely used in practice. Predictive equations are the alternatives. Objectives: To assess the accuracy of 14 predictive resting energy expenditure(REE) equations in ventilated Thai patients. Methods: We compared the accuracy and agreement of 14 equations. The equations included the American College of Chest Physicians(ACCP) equation, Harris–Benedict equation(HBE), 1.2×HBE, 1.5×HBE, Mifflin–St. Jeor(MSJ), Ireton-Jones 1992 and 2002, Penn State 2003(HBE and MSJ) and 2010, Swinamer 1990, Faisy, Brandi 1999, and 25 kcal/kg equation. An equation was ascertained as accurate if the calculated values fell within ±10% of the measured REEs. Spearman correlation coefficient, Bland–Altman method, and intraclass correlation coefficient were used to analysis. Results: We obtained data from 24 ventilated patients undergoing REE measurement by IC. Fifty percent of them were male with a median age of 64.5 years, a median height of 160 cm, and a median body mass index of 22.95 kg/m2. The predictive precision of all equations was poor, with largely different accuracies from 6.7% to 48.1%. The most reliable equation was Penn State 2010. The ACCP, HBE, MSJ, and Penn State 2003(HBE) tended to underestimate calorie need. Contrastingly, the other equations tended to overestimate REEs. Despite a moderate degree of correlations, the Bland–Altman plots demonstrated clinically unacceptable discrepancies between measured REE and REE calculated by each equation. Conclusions: In ventilated Thai patients, there were no precise equations for determining REE.

Indirect calorimetry (IC) is the gold standard for determining calorie need in critically ill patients [1][2][3]. However, it is rarely affordable in Thailand. In routine clinical practice, predictive equations are the alternatives. The precision of previously validated equations in the estimation of energy requirements in critically ill patients varied from 37% to 65% of measured resting energy expenditure (REE) [4]. Among the various equations available for use in determining the calorie demand and thus achieving nutritional support optimization, Thai physicians commonly use the American College of Chest Physicians (ACCP) equation [5]. However, the use of any of these equations might result in inaccurate estimation of REE in Thai patients. To our knowledge, there has been no equation validated for mechanically ventilated Thai patients.

Study design
We conducted a retrospective study of the accuracy of predictive REE in mechanically ventilated Thai patients. We reviewed data, including measured REEs and other variables used for REE calculation, pertaining to ventilated patients admitted in medical intensive care units in King Chulalongkorn Memorial Hospital between June 2014 and May 2016. We determined the accuracy and agreement of 14 predictive equations, as indicated in Table 1 [4][5][6][7][8][9][10][11][12][13]. The clinical variables used for the calculations involved in the equations, such as heart rate (HR), minute ventilation, body temperature (BT), etc., were derived from the same REE measurement period. This study was approved by the Institutional Review Board (IRB) of the Faculty of Medicine, Chulalongkorn University, for authorization for medical record review (certificate of approval no. 493/60). The clinical trial registration number was TCTR20171014004.

Eligibility criteria
This study included data from ventilated Thai patients who underwent REE measurement by IC, using an E-COVX module integrated in Engstrom ventilators, GE healthcare. The E-COVX calculates REE by measuring oxygen consumption (VO 2 ) and carbon dioxide production (VCO 2 ). The VO 2 , VCO 2 , and REE values were continuously measured and calculated throughout the day. The average REE value was recorded once VO 2 and VCO 2 were first steady (within a ±5% coefficient of variation over a 20 min period). Additionally, the suction and ventilator setting adjustment procedures were not performed within a 2 h period prior to collecting REE data. To obtain precise measured values, we excluded data from patients with the following conditions: (1) hemodynamic instability (systolic blood pressure <90 mmHg or a decrease in systolic blood pressure >40 mmHg or mean arterial pressure <65 mmHg), (2) respiratory instability or variable respiratory patterns (>20% fluctuation of respiratory rate (RR) or respiratory rate >35/min), (3) high level of ventilator setting: FiO 2 >60% or positive end expiratory pressure (PEEP) >12 cmH 2 O, (4) variations in carbon dioxide pool, such as patients receiving continuous hemodialysis or bicarbonate infusion, and (5) an air-leakage problem, including leaks around ventilator circuits and leaks in chest drainage system [14].

Statistical analyses
The accuracy of predictive equations was analyzed based on the following norm: A predictive equation was deemed to be accurate when the calculated values arising pursuant to its use fell within ±10% of the measured REE. Results were expressed as numbers and percentages of patients with precise, overestimated, and underestimated REEs, which were calculated by each equation. The discrepancy between estimated and measured values was expressed as a mean percentage error (bias%). Spearman correlation coefficient and the Bland-Altman method were used to test the association and agreement between these compared values [15]. The intraclass correlation coefficient (ICC) (2-way mixed effect) was also used to assess inter-rater agreement. We also compared the accuracy of equations in obese and non-obese patients. Additionally, we used the STARD checklist when writing our report [16].

Results
Thirty-one measured REEs were obtained from 25 ventilated Thai patients. One patient was excluded due to air leakage during IC measurement (Figure 1). In subgroup analysis, the Penn State 2010 equation provided the most accurate estimation of calorie need in obese patients (BMI ≥ 30 kg/m 2 ), while the HBE provided the most reliable prediction of REE in non-obese patients (Figure 2).
Accordingly, on Bland-Altman plots (Figure 3), there was a poor agreement between the calculated and measured values. Contrastingly, the calculated values had moderately to strongly positive correlations with measured values (r = 0.445-0.778,  Brandi 1999, and the 25 kcal/kg actual body weight had moderate reliability. Only ACCP had poor reliability ( Table 6). (Continued)

Discussion
Our study demonstrated that no predictive equations accurately estimated calorie demand in ventilated Thai patients, and none had clinically acceptable accuracy, despite the fact that calculated values had moderately to strongly positive correlations with measured values and moderate to good reliability in the ICC analysis for inter-rater agreement measurement. However, HBE more reliably predicted REE in underweight (BMI < 18.5 kg/m 2 ) and non-obese (BMI < 30 kg/    Accordingly, the discrepancy between measured and predictive REEs could be explained by the following reasons. First, most equations use body weight (BW) as the basis in their estimation of calorie requirement. Practically, it is difficult to obtain the precise actual weight in mechanically ventilated patients. Commonly, excessive total body water from fluid resuscitation or decreased muscular mass confounds the real values. Second, dynamic changes in clinical conditions result in variable metabolic demands, which depend on phases of critical illness. Furthermore, most equations are validated for healthy volunteers, spontaneously breathing patients, and non-Asian populations, who possibly have metabolic differences from Thais. Finally, there are several factors pertaining to the ICU that might be expected to confound measured values, for example, recent adjustment of the ventilator setting before IC measurement [18], or measurement of calories at different points of time or under the prevalence of different feeding conditions, including fasting, intermittent feeding, or continuous feeding.
Although the predictive equations were initially developed in the western populations, these equations did not precisely estimate REE in mechanically ventilated western patients. Similarly, all of the equations had poor predictive precision in mechanically ventilated Thai patients. Therefore, REE should be assessed using IC. However, there were some limitations in this study. First, feeding conditions at the time of IC measurement were not recorded. Additionally, the REE was measured during a stable condition, which did not represent energy need in an active phase of illness. Furthermore, there was a selective bias resulting from the fact that physicians usually performed IC in patients with extreme BMI or uncertain actual BW. Finally, the study lacked statistical power due to a small sample size.

Conclusion
The accuracy of the predictive equations was poor, and thus this method of calorie-need estimation was unable to replace IC in the determination of calorie requirement in mechanically ventilated Thai patients.  The ICC values of less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 are indicative of poor, moderate, good, and excellent reliability, respectively.
Author contributions. NK contributed to the conception and design of this study, and the acquisition of data. NK also analyzed and interpreted the data. NK wrote and revised the manuscript, approved the final version submitted for publication, and takes full responsibility for the statements made in the published article.